Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object Detection
Downsampling input images is a simple trick to speed up visual object-detection algorithms, especially on robotic vision and applied mobile vision systems. However, this trick comes with a significant decline in accuracy. In this paper, dual-resolution dual-path Convolutional Neural Networks (CNNs),...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/14/3111 |
| _version_ | 1798040486240321536 |
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| author | Jing Pan Hanqing Sun Zhanjie Song Jungong Han |
| author_facet | Jing Pan Hanqing Sun Zhanjie Song Jungong Han |
| author_sort | Jing Pan |
| collection | DOAJ |
| description | Downsampling input images is a simple trick to speed up visual object-detection algorithms, especially on robotic vision and applied mobile vision systems. However, this trick comes with a significant decline in accuracy. In this paper, dual-resolution dual-path Convolutional Neural Networks (CNNs), named DualNets, are proposed to bump up the accuracy of those detection applications. In contrast to previous methods that simply downsample the input images, DualNets explicitly take dual inputs in different resolutions and extract complementary visual features from these using dual CNN paths. The two paths in a DualNet are a backbone path and an auxiliary path that accepts larger inputs and then rapidly downsamples them to relatively small feature maps. With the help of the carefully designed auxiliary CNN paths in DualNets, auxiliary features are extracted from the larger input with controllable computation. Auxiliary features are then fused with the backbone features using a proposed progressive residual fusion strategy to enrich feature representation.This architecture, as the feature extractor, is further integrated with the Single Shot Detector (SSD) to accomplish latency-sensitive visual object-detection tasks. We evaluate the resulting detection pipeline on Pascal VOC and MS COCO benchmarks. Results show that the proposed DualNets can raise the accuracy of those CNN detection applications that are sensitive to computation payloads. |
| first_indexed | 2024-04-11T22:08:16Z |
| format | Article |
| id | doaj.art-ab82ea8a24ba44d9950ffc51e2308c54 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:08:16Z |
| publishDate | 2019-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ab82ea8a24ba44d9950ffc51e2308c542022-12-22T04:00:38ZengMDPI AGSensors1424-82202019-07-011914311110.3390/s19143111s19143111Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object DetectionJing Pan0Hanqing Sun1Zhanjie Song2Jungong Han3School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Electrical and Information Engineering, Tianjin University, Tianjin 300072, ChinaSchool of Mathematics, Tianjin University, Tianjin 300072, ChinaWMG Data Science, University of Warwick, Conventry CV4 7AL, UKDownsampling input images is a simple trick to speed up visual object-detection algorithms, especially on robotic vision and applied mobile vision systems. However, this trick comes with a significant decline in accuracy. In this paper, dual-resolution dual-path Convolutional Neural Networks (CNNs), named DualNets, are proposed to bump up the accuracy of those detection applications. In contrast to previous methods that simply downsample the input images, DualNets explicitly take dual inputs in different resolutions and extract complementary visual features from these using dual CNN paths. The two paths in a DualNet are a backbone path and an auxiliary path that accepts larger inputs and then rapidly downsamples them to relatively small feature maps. With the help of the carefully designed auxiliary CNN paths in DualNets, auxiliary features are extracted from the larger input with controllable computation. Auxiliary features are then fused with the backbone features using a proposed progressive residual fusion strategy to enrich feature representation.This architecture, as the feature extractor, is further integrated with the Single Shot Detector (SSD) to accomplish latency-sensitive visual object-detection tasks. We evaluate the resulting detection pipeline on Pascal VOC and MS COCO benchmarks. Results show that the proposed DualNets can raise the accuracy of those CNN detection applications that are sensitive to computation payloads.https://www.mdpi.com/1424-8220/19/14/3111dual-resolutionCNNvisual object detectionprogressive fusion |
| spellingShingle | Jing Pan Hanqing Sun Zhanjie Song Jungong Han Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object Detection Sensors dual-resolution CNN visual object detection progressive fusion |
| title | Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object Detection |
| title_full | Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object Detection |
| title_fullStr | Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object Detection |
| title_full_unstemmed | Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object Detection |
| title_short | Dual-Resolution Dual-Path Convolutional Neural Networks for Fast Object Detection |
| title_sort | dual resolution dual path convolutional neural networks for fast object detection |
| topic | dual-resolution CNN visual object detection progressive fusion |
| url | https://www.mdpi.com/1424-8220/19/14/3111 |
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